Road to ML: From Zero to Hero

A comprehensive, step-by-step guide to learning Machine Learning from absolute basics to advanced topics

Perfect for beginners • 26 Learning Modules • 23 Real-World Projects • Production-Ready Skills

Start Here • How to Use the Study Hub • Foundation & job readiness • Get Started • Learning Path • Projects • System Design • Full-stack AI • Contribute

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Watch the full playlist on YouTube

Why This Repository?

• Zero to Hero Path: Complete learning journey from basics to advanced ML
• Beginner-Friendly: No prior experience needed. We cover everything
• Hands-On Learning: 23 practical projects to build your portfolio
• Production Focus: Learn deployment, MLOps, and real-world skills
• Well-Organized: Logical progression with clear learning objectives
• Community-Driven: Open source, contributions welcome!

Perfect for: Students, career switchers, self-learners, and anyone wanting to master ML

Career Paths

Choose your path! This repository prepares you for multiple ML/AI careers. Select your target role to see a customized learning path:

Role	Focus	Est. Time	Key Modules	Full Guide
Data Analyst	Insights & Reports	8-12 months	00, 01, 19, 20, 21	View Guide →
Data Scientist	Predictive Models	13-20 months	00-08, 15, 19-21	View Guide →
ML Engineer	Production ML	17-26 months	00-10, 13-14, 19-21	View Guide →
LLM Engineer	Language Models	17-24 months	00-01, 05, 09-10, 12, 25, 13-14, 19	View Guide →
GenAI Solution Architect	Production GenAI	15-21 months	00-01, 02, 05, 09-10, 12, 25, 13-14, 19	View Guide →
Computer Vision Engineer	Image Processing	16-25 months	00-01, 04-05, 09-11, 13-14, 19, 21	View Guide →
AI Engineer	Generalist AI	25-38 months	00-15, 19-21, 22-24, 25	View Guide →
Data Engineer	Data Infrastructure	14-20 months	00-01, 13-14, 19-20	View Guide →
MLOps Engineer	ML Operations	16-24 months	00-01, 05, 09-10, 12, 25, 13-14, 19	View Guide →

Foundational side track: System Design for Beginners: 31 lessons covering HTTP, TCP, DNS, caching, load balancing, CAP, sharding, and message queues, plus a capstone and 9 interview-style design problems (rate limiter, social feed, chat, video, file storage, maps, KV store, message queue, and a real-time ML feature store). Useful for ML Engineer, MLOps, Data Engineer, AI Engineer, and Full-Stack AI Engineer paths. Pairs with the ML System Design Guide, which applies these foundations to ML services. For application backend engineering (auth, Postgres, queues, ops), use the Backend Engineer Roadmap alongside Phases B/C/E of the Full-Stack Track.

Time assumptions: Ranges in the Est. Time column assume about 10–15 hours/week for a steady part-time pace and 30–40 hours/week for an intensive full-time pace, unless a row states otherwise (for example, Full-Stack AI Engineer calls out parallel blueprint hours).

SQL timing: Module 19 is Stage 1.5 (parallel with Modules 01–02) for most roles, not after GenAI. Module lists like 19-21 mean you need all three modules, not that SQL comes last.

What each path includes:

• Career Roadmap Guide roles: module priorities, suggested projects, essential resources, skills checklists, and time guidance grounded in this repo’s 00–25 folders.
• Full-Stack AI Engineer: same style of overview inside the Career Roadmap Guide, plus in-repo lesson chapters and the companion Full-Stack AI Engineer Blueprint for TypeScript through deployment and AI integration. Most hands-on engineering work is outside the numbered module folders; use the lessons + blueprint phases and free links for day-to-day study.

The full roadmap guide follows the same module numbering and Stage 6 vs. module 15 (time series) branch as the Learning Path Overview above.

View Complete Career Roadmap Guide →

Full-stack AI quick start

If you want ML plus product engineering (TypeScript, APIs, databases, Next.js, containers, and AI features in production), use this sequence:

1. Skim the role overview: Full-Stack AI Engineer
2. Work through in-repo lesson chapters (Phases A–H)
3. Follow the Full-Stack AI Engineer Blueprint for deliverables, external free links, and portfolio milestones

Keep progressing through the numbered 00–25 modules for core ML depth in parallel when you can.

System design foundations

If you're heading toward ML Engineer, MLOps, Data Engineer, AI Engineer, or Full-Stack AI Engineer, you'll need backend system design vocabulary (HTTP, TCP, caching, load balancing, CAP, sharding, message queues). Read these in order:

1. System Design for Beginners: 22 foundation lessons + 9 interview-style designs (~8 hours total).
2. ML System Design Guide: how those fundamentals apply to ML services (latency, serving, drift, MLOps).
3. Interview Preparation Guide: practice questions in the System Design section.

You don't need to finish all 26 ML modules first. The system design track is parallel and pairs well with module 13 (deployment), 14 (MLOps), and any project phase (16–18).

Table of Contents

• Overview
• Learning Path
• Prerequisites
• Getting Started
• Repository Structure
• Resources
• YouTube Playlist
• Career Paths
• Full-stack AI quick start
• System design foundations
• Contributing

Overview

This repository provides a structured learning path for machine learning, organized in a logical progression from fundamentals to advanced topics. Each module includes:

• Clear explanations of concepts
• Hands-on exercises with solutions
• Practical projects to reinforce learning
• Additional resources for deeper understanding

Learning Path

Follow stages, not folder numbers. Folders 13–14 (deployment) appear before 19 (SQL) on disk. That is intentional for repo layout, not teaching order. Use START-HERE.md and FOUNDATION_AND_JOB_READINESS.md. SQL is Stage 1.5 for most job tracks.

Note on numbering: Folder names use Module 00–25 (for example 09-neural-networks-basics). Stages in the table below are the recommended learning sequence. A stage can span several modules. They are not the same label. Some modules can be learned in parallel depending on your goals. See each module README for prerequisites.

Time Estimates: Realistic completion time is 15–22 months full-time (30–40 hrs/week) or 30–39 months part-time (10–15 hrs/week) for full coverage of all 26 modules and 23 projects. See the FAQ section for a stage-by-stage breakdown.

How this repo lines up with typical DS/ML curricula

Structured courses usually stack Python, then math and stats, then tabular ML, then evaluation and feature work, then deep learning and GenAI, then SQL and storytelling, then production and MLOps, then electives. This repository follows the same logic but groups topics by module folder instead of week numbers. Stage 0 covers programming plus calculus, linear algebra, and stats. Stage 1 covers NumPy, Pandas, visualization, EDA, APIs, and SQL access. Stages 2–4 cover classical supervised and unsupervised ML. Stages 5–7 cover neural nets, specialized deep learning, and LLM-era tooling. Modules 13–14 cover deployment and experiment discipline. Module 15 and modules 22–24 are branch tracks for time series, RL, graphs, and audio. If your external syllabus mentions MLE and odds for logistic models, ROC vs PR, data leakage, Airflow or Kubernetes monitoring, or Kaggle-style iteration, look first in modules 04, 05, 06–07, 14, and the project folders.

Learning Path Overview

Stage	Modules	Focus Area	Est. Time (Full-Time)	Est. Time (Part-Time)
Stage 0	00	Foundation (Python, Math)	2-3 months	4-6 months
Stage 1	01	Data Fundamentals	2-3 months	4-6 months
Stage 1.5	19	SQL & Databases (parallel with 1–2 for job tracks)	1-2 months	2-3 months
Stage 2	02-05	ML Basics	2-3 months	4-6 months
Stage 3	06-07	Advanced ML	1-2 months	2-4 months